Journal
COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION
Volume 10, Issue 6, Pages 663-674Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/21681163.2021.2021111
Keywords
Diabetic retinopathy; transfer learning; automatic diagnosis; fundus images; deep neural network
Categories
Funding
- National Natural Science Foundation of China [11527801, 41706201]
Ask authors/readers for more resources
Diabetic retinopathy (DR) is a leading cause of visual impairment and blindness worldwide. This study proposes a novel two-stage framework using deep neural networks for automatic DR classification, achieving state-of-the-art performance in identifying retinal biomarkers and detecting DR.
Diabetic retinopathy (DR) is an ocular manifestation of diabetes and the leading cause of visual impairment and blindness across the globe. Early detection and treatment of DR can salvage from visual impairment. The manual screening of DR is a very laborious and time-intensive effort and heavily dependent on professional ophthalmologists. In addition, the subtle distinction among various retinal biomarkers and different grades of DR makes this recognition very challenging. To address the aforementioned problem, deep neural networks have brought many revolutions in the last few years. In this study, we proposed a novel two-stage framework for automatic DR classification. In the first stage, we employed two distinct U-Net models for optic disc (OD) and blood vessel (BV) segmentation during the preprocessing. In the second stage, the enhanced retinal images after OD and BV extraction are used as an input of transfer learning-based model VGGNet, which performs DR detection by identifying retinal biomarkers such as microaneurysms (MA), haemorrhages (HM), and exudates (EX). The proposed model achieved state-of-the-art performance with an average accuracy of 96.60%, 93.95%, 92.25% evaluated on EyePACS-1, Messidor-2, and DIARETDB0, respectively. Extensive experiments and comparison with baseline methods demonstrate that the effectiveness of the proposed approach.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available